TF-MoDISco results

Summary of motifs

TOMTOM matches to motifs

In [1]:
import os
import sys
sys.path.append(os.path.abspath("/users/amtseng/tfmodisco/src/"))
from tfmodisco.run_tfmodisco import import_shap_scores, import_tfmodisco_results
from motif.read_motifs import pfm_info_content, pfm_to_pwm, trim_motif_by_ic
from motif.match_motifs import match_motifs_to_database
from util import figure_to_vdom_image
import plot.viz_sequence as viz_sequence
import numpy as np
import h5py
import matplotlib.pyplot as plt
import vdom.helpers as vdomh
from IPython.display import display

Define constants and paths

In [2]:
# Define parameters/fetch arguments
tf_name = os.environ["TFM_TF_NAME"]
shap_scores_path = os.environ["TFM_SHAP_PATH"]
tfm_results_path = os.environ["TFM_TFM_PATH"]
hyp_score_key = os.environ["TFM_HYP_SCORE_KEY"]
if "TFM_MOTIF_CACHE" in os.environ:
    tfm_motifs_cache_dir = os.environ["TFM_MOTIF_CACHE"]
else:
    tfm_motifs_cache_dir = None

print("TF name: %s" % tf_name)
print("DeepSHAP scores path: %s" % shap_scores_path)
print("TF-MoDISco results path: %s" % tfm_results_path)
print("Importance score key: %s" % hyp_score_key)
print("Saved TF-MoDISco-derived motifs cache: %s" % tfm_motifs_cache_dir)

TF name: E2F6
DeepSHAP scores path: /users/amtseng/tfmodisco/results/importance_scores/multitask_profile/E2F6_multitask_profile_fold10/E2F6_multitask_profile_fold10_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/E2F6_multitask_profile_fold10/E2F6_multitask_profile_fold10_profile_tfm.h5
Importance score key: profile_hyp_scores
Saved TF-MoDISco-derived motifs cache: /users/amtseng/tfmodisco/results/reports/tfmodisco_results//cache/multitask_profile/E2F6_multitask_profile_fold10/E2F6_multitask_profile_fold10_profile
In [3]:
# Define paths and constants
input_length = 2114
shap_score_center_size = 400
In [4]:
if tfm_motifs_cache_dir:
    os.makedirs(tfm_motifs_cache_dir, exist_ok=True)

Import SHAP scores and TF-MoDISco results

In [5]:
# Import SHAP coordinates and one-hot sequences
hyp_scores, _, one_hot_seqs, shap_coords = import_shap_scores(shap_scores_path, hyp_score_key, center_cut_size=shap_score_center_size)
# This cuts the sequences/scores off just as how TF-MoDISco saw them, but the coordinates are uncut

Importing SHAP scores: 100%|██████████| 52/52 [00:35<00:00,  1.45it/s]
In [6]:
# Import the TF-MoDISco results object
tfm_obj = import_tfmodisco_results(tfm_results_path, hyp_scores, one_hot_seqs, shap_score_center_size)

Plot some SHAP score tracks

Plot the central region of some randomly selected actual importance scores

In [7]:
plot_slice = slice(int(shap_score_center_size / 4), int(3 * shap_score_center_size / 4))
for index in np.random.choice(hyp_scores.shape[0], size=5, replace=False):
    viz_sequence.plot_weights((hyp_scores[index] * one_hot_seqs[index])[plot_slice], subticks_frequency=100)

Plot TF-MoDISco results

Plot all motifs by metacluster

In [8]:
motif_pfms, motif_hcwms, motif_cwms = [], [], []  # Save the trimmed PFMs, hCWMs, and CWMs
motif_pfms_short = []  # PFMs that are even more trimmed (for TOMTOM)
num_seqlets = []  # Number of seqlets for each motif
motif_seqlets = []  # Save seqlets of each motif
metaclusters = tfm_obj.metacluster_idx_to_submetacluster_results
num_metaclusters = len(metaclusters.keys())
if tfm_motifs_cache_dir:
    motif_hdf5 = h5py.File(os.path.join(tfm_motifs_cache_dir, "all_motifs.h5"), "w")
for metacluster_i, metacluster_key in enumerate(metaclusters.keys()):
    metacluster = metaclusters[metacluster_key]
    display(vdomh.h3("Metacluster %d/%d" % (metacluster_i + 1, num_metaclusters)))
    patterns = metacluster.seqlets_to_patterns_result.patterns
    if not patterns:
        break
    motif_pfms.append([])
    motif_hcwms.append([])
    motif_cwms.append([])
    motif_pfms_short.append([])
    num_seqlets.append([])
    motif_seqlets.append([])
    num_patterns = len(patterns)
    for pattern_i, pattern in enumerate(patterns):
        seqlets = pattern.seqlets
        display(vdomh.h4("Pattern %d/%d" % (pattern_i + 1, num_patterns)))
        display(vdomh.p("%d seqlets" % len(seqlets)))
        
        pfm = pattern["sequence"].fwd
        hcwm = pattern["task0_hypothetical_contribs"].fwd
        cwm = pattern["task0_contrib_scores"].fwd
        
        pfm_fig = viz_sequence.plot_weights(pfm, subticks_frequency=10, return_fig=True)
        hcwm_fig = viz_sequence.plot_weights(hcwm, subticks_frequency=10, return_fig=True)
        cwm_fig = viz_sequence.plot_weights(cwm, subticks_frequency=10, return_fig=True)
        pfm_fig.tight_layout()
        hcwm_fig.tight_layout()
        cwm_fig.tight_layout()
        
        motif_table = vdomh.table(
            vdomh.tr(
                vdomh.td("Sequence (PFM)"),
                vdomh.td(figure_to_vdom_image(pfm_fig))
            ),
            vdomh.tr(
                vdomh.td("Hypothetical contributions (hCWM)"),
                vdomh.td(figure_to_vdom_image(hcwm_fig))
            ),
            vdomh.tr(
                vdomh.td("Actual contributions (CWM)"),
                vdomh.td(figure_to_vdom_image(cwm_fig))
            )
        )
        display(motif_table)
        plt.close("all")  # Remove all standing figures
        
        # Trim motif based on information content
        short_trimmed_pfm = trim_motif_by_ic(pfm, pfm)
        motif_pfms_short[-1].append(short_trimmed_pfm)
        
        # Expand trimming to +/- 4bp on either side
        trimmed_pfm = trim_motif_by_ic(pfm, pfm, pad=4)
        trimmed_hcwm = trim_motif_by_ic(pfm, hcwm, pad=4)
        trimmed_cwm = trim_motif_by_ic(pfm, cwm, pad=4)
        
        motif_pfms[-1].append(trimmed_pfm)
        motif_hcwms[-1].append(trimmed_hcwm)
        motif_cwms[-1].append(trimmed_cwm)
        
        num_seqlets[-1].append(len(seqlets))
        
        if tfm_motifs_cache_dir:
            # Save results and figures
            motif_id = "%d_%d" % (metacluster_i, pattern_i)
            pfm_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_pfm_full.png"))
            hcwm_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_hcwm_full.png"))
            cwm_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_cwm_full.png"))
            motif_dset = motif_hdf5.create_group(motif_id)
            motif_dset.create_dataset("pfm_full", data=pfm, compression="gzip")
            motif_dset.create_dataset("hcwm_full", data=hcwm, compression="gzip")
            motif_dset.create_dataset("cwm_full", data=cwm, compression="gzip")
            motif_dset.create_dataset("pfm_trimmed", data=trimmed_pfm, compression="gzip")
            motif_dset.create_dataset("hcwm_trimmed", data=trimmed_hcwm, compression="gzip")
            motif_dset.create_dataset("cwm_trimmed", data=trimmed_cwm, compression="gzip")
            motif_dset.create_dataset("pfm_short_trimmed", data=short_trimmed_pfm, compression="gzip")

Metacluster 1/1

Pattern 1/12

5272 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 2/12

3076 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 3/12

1644 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 4/12

821 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 5/12

599 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 6/12

327 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 7/12

241 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 8/12

207 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 9/12

40 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 10/12

38 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 11/12

34 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 12/12

30 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Summary of motifs

Motifs are trimmed based on information content, and presented in descending order by number of supporting seqlets. The motifs are separated by metacluster. The motifs are presented as hCWMs. The forward orientation is defined as the orientation that is richer in purines.

In [9]:
colgroup = vdomh.colgroup(
    vdomh.col(style={"width": "5%"}),
    vdomh.col(style={"width": "5%"}),
    vdomh.col(style={"width": "45%"}),
    vdomh.col(style={"width": "45%"})
)
header = vdomh.thead(
    vdomh.tr(
        vdomh.th("#", style={"text-align": "center"}),
        vdomh.th("Seqlets", style={"text-align": "center"}),
        vdomh.th("Forward", style={"text-align": "center"}),
        vdomh.th("Reverse", style={"text-align": "center"})
    )
)

for i in range(len(motif_hcwms)):
    display(vdomh.h3("Metacluster %d/%d" % (i + 1, num_metaclusters)))
    body = []
    for j in range(len(motif_hcwms[i])):
        motif = motif_hcwms[i][j]
        if np.sum(motif[:, [0, 2]]) > 0.5 * np.sum(motif):
            # Forward is purine-rich, reverse-complement is pyrimidine-rich
            f, rc = motif, np.flip(motif, axis=(0, 1))
        else:
            f, rc = np.flip(motif, axis=(0, 1)), motif
            
        f_fig = viz_sequence.plot_weights(f, figsize=(20, 4), return_fig=True)
        f_fig.tight_layout()
        rc_fig = viz_sequence.plot_weights(rc, figsize=(20, 4), return_fig=True)
        rc_fig.tight_layout()
        
        if tfm_motifs_cache_dir:
            # Save results and figures
            motif_id = "%d_%d" % (i, j)
            f_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_hcwm_trimmed_fwd.png"))
            rc_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_hcwm_trimmed_rev.png"))

        body.append(
            vdomh.tr(
                vdomh.td(str(j + 1)),
                vdomh.td(str(num_seqlets[i][j])),
                vdomh.td(figure_to_vdom_image(f_fig)),
                vdomh.td(figure_to_vdom_image(rc_fig))
            )
        )
    display(vdomh.table(colgroup, header, vdomh.tbody(*body)))
    plt.close("all")

Metacluster 1/1

/users/amtseng/tfmodisco/src/plot/viz_sequence.py:152: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  fig = plt.figure(figsize=figsize)
#SeqletsForwardReverse
15272
23076
31644
4821
5599
6327
7241
8207
940
1038
1134
1230

Top TOMTOM matches for each motif

Here, the TF-MoDISco motifs are plotted as hCWMs, but the TOMTOM matches are shown as PWMs.

In [10]:
num_matches_to_keep = 10
num_matches_to_show = 5

header = vdomh.thead(
    vdomh.tr(
        vdomh.th("Motif ID", style={"text-align": "center"}),
        vdomh.th("q-val", style={"text-align": "center"}),
        vdomh.th("PWM", style={"text-align": "center"})
    )
)

for i in range(len(motif_pfms)):
    display(vdomh.h3("Metacluster %d/%d" % (i + 1, num_metaclusters)))
    
    # Compute TOMTOM matches for all motifs in the metacluster at once
    out_dir = os.path.join(tfm_motifs_cache_dir, "tomtom", "metacluster_%d" % i) if tfm_motifs_cache_dir else None
    tomtom_matches = match_motifs_to_database(motif_pfms_short[i], top_k=num_matches_to_keep, temp_dir=out_dir)
    
    for j in range(len(motif_pfms[i])):
        display(vdomh.h4("Motif %d/%d" % (j + 1, len(motif_pfms[i]))))
        viz_sequence.plot_weights(motif_hcwms[i][j])
    
        body = []
        for k, (match_name, match_pfm, match_qval) in enumerate(tomtom_matches[j]):
            fig = viz_sequence.plot_weights(pfm_to_pwm(match_pfm), return_fig=True)
            fig.tight_layout()
            if k < num_matches_to_show:
                body.append(
                    vdomh.tr(
                        vdomh.td(match_name),
                        vdomh.td(str(match_qval)),
                        vdomh.td(figure_to_vdom_image(fig))
                    )
                )
                if tfm_motifs_cache_dir:
                    # Save results and figures
                    motif_id = "%d_%d" % (i, j)
                    fig.savefig(os.path.join(out_dir, motif_id + ("_hit-%d.png" % (k + 1))))
            else:
                body.append(
                    vdomh.tr(
                        vdomh.td(match_name),
                        vdomh.td(str(match_qval)),
                        vdomh.td("Not shown")
                    )
                )
        if not body:
            display(vdomh.p("No TOMTOM matches passing threshold"))
        else:
            display(vdomh.table(header, vdomh.tbody(*body)))
        plt.close("all")

Metacluster 1/1

Motif 1/12

Motif IDq-valPWM
MA0059.1_MAX::MYC0.000553829
MAX_HUMAN.H11MO.0.A0.00065164
MA0147.3_MYC0.00065164
MXI1_HUMAN.H11MO.1.A0.00065164
MXI1_HUMAN.H11MO.0.A0.0007563489999999999
MYCN_HUMAN.H11MO.0.A0.00161552Not shown
MA0058.3_MAX0.00161552Not shown
MYC_HUMAN.H11MO.0.A0.00161552Not shown
MA0004.1_Arnt0.00621354Not shown
MA0825.1_MNT0.00621354Not shown

Motif 2/12

Motif IDq-valPWM
MA0471.2_E2F60.000143395
E2F1_HUMAN.H11MO.0.A0.000143395
E2F3_HUMAN.H11MO.0.A0.00018411099999999999
MA0758.1_E2F70.000606287
MA0865.1_E2F80.0007384360000000001
E2F4_HUMAN.H11MO.1.A0.0007384360000000001Not shown
TFDP1_HUMAN.H11MO.0.C0.0007384360000000001Not shown
E2F6_HUMAN.H11MO.0.A0.000995065Not shown
E2F4_HUMAN.H11MO.0.A0.00137961Not shown
MA1122.1_TFDP10.00155416Not shown

Motif 3/12

Motif IDq-valPWM
MA1650.1_ZBTB140.00958868
SP2_HUMAN.H11MO.0.A0.016655200000000002
MXI1_HUMAN.H11MO.0.A0.016655200000000002
SP1_HUMAN.H11MO.0.A0.016655200000000002
SP3_HUMAN.H11MO.0.B0.016655200000000002
AP2D_HUMAN.H11MO.0.D0.0479196Not shown
THAP1_HUMAN.H11MO.0.C0.048418199999999995Not shown
USF2_HUMAN.H11MO.0.A0.05289730000000001Not shown
KLF16_HUMAN.H11MO.0.D0.0719391Not shown
KLF3_HUMAN.H11MO.0.B0.092627Not shown

Motif 4/12

No TOMTOM matches passing threshold

Motif 5/12

Motif IDq-valPWM
MA1099.2_HES10.42756400000000006
MA0104.4_MYCN0.42756400000000006
MA0632.2_TCFL50.42756400000000006
MYCN_HUMAN.H11MO.0.A0.42756400000000006
MA1516.1_KLF30.42756400000000006
MYC_HUMAN.H11MO.0.A0.42756400000000006Not shown
CR3L1_HUMAN.H11MO.0.D0.42756400000000006Not shown
MXI1_HUMAN.H11MO.1.A0.42756400000000006Not shown
MA0608.1_Creb3l20.42756400000000006Not shown
MAX_HUMAN.H11MO.0.A0.42756400000000006Not shown

Motif 6/12

Motif IDq-valPWM
CTCFL_HUMAN.H11MO.0.A2.4267399999999997e-05
CTCF_HUMAN.H11MO.0.A0.00015019999999999996
MA1102.2_CTCFL0.001925
MA0139.1_CTCF0.00196551
MA0155.1_INSM10.15893
MXI1_HUMAN.H11MO.0.A0.15893Not shown
USF2_HUMAN.H11MO.0.A0.15893Not shown
ZFX_HUMAN.H11MO.0.A0.15893Not shown
MA0104.4_MYCN0.15893Not shown
SP1_HUMAN.H11MO.0.A0.15893Not shown

Motif 7/12

Motif IDq-valPWM
SP2_HUMAN.H11MO.0.A0.000873967
SP3_HUMAN.H11MO.0.B0.00240323
MA1650.1_ZBTB140.00240323
USF2_HUMAN.H11MO.0.A0.00240323
SP1_HUMAN.H11MO.0.A0.00240323
MXI1_HUMAN.H11MO.0.A0.00480252Not shown
THAP1_HUMAN.H11MO.0.C0.00648178Not shown
SP1_HUMAN.H11MO.1.A0.0105402Not shown
KLF3_HUMAN.H11MO.0.B0.011991799999999999Not shown
AP2D_HUMAN.H11MO.0.D0.0138066Not shown

Motif 8/12

Motif IDq-valPWM
SP2_HUMAN.H11MO.0.A0.000415381
SP3_HUMAN.H11MO.0.B0.00173488
SP1_HUMAN.H11MO.0.A0.00213502
AP2D_HUMAN.H11MO.0.D0.00213502
MXI1_HUMAN.H11MO.0.A0.00356564
THAP1_HUMAN.H11MO.0.C0.00569592Not shown
MA1650.1_ZBTB140.00873962Not shown
MA1513.1_KLF150.0219315Not shown
USF2_HUMAN.H11MO.0.A0.0219703Not shown
SP1_HUMAN.H11MO.1.A0.03963Not shown

Motif 9/12

Motif IDq-valPWM
SP2_HUMAN.H11MO.0.A0.000528056
SP1_HUMAN.H11MO.0.A0.00140844
SP3_HUMAN.H11MO.0.B0.00140844
MXI1_HUMAN.H11MO.0.A0.00196042
MA1513.1_KLF150.0119484
MA1650.1_ZBTB140.0119484Not shown
KLF16_HUMAN.H11MO.0.D0.0119484Not shown
USF2_HUMAN.H11MO.0.A0.011972Not shown
THAP1_HUMAN.H11MO.0.C0.0220653Not shown
KLF3_HUMAN.H11MO.0.B0.0253595Not shown

Motif 10/12

Motif IDq-valPWM
ZN770_HUMAN.H11MO.0.C0.00105671
ZN770_HUMAN.H11MO.1.C0.0641582
ZN263_HUMAN.H11MO.0.A0.120323
MA1587.1_ZNF1350.129064
KLF6_HUMAN.H11MO.0.A0.143316
KLF15_HUMAN.H11MO.0.A0.143316Not shown
MA1596.1_ZNF4600.143316Not shown
MAZ_HUMAN.H11MO.1.A0.248177Not shown
IKZF1_HUMAN.H11MO.0.C0.27411399999999997Not shown
COT1_HUMAN.H11MO.0.C0.27411399999999997Not shown

Motif 11/12

Motif IDq-valPWM
CPEB1_HUMAN.H11MO.0.D3.0698600000000005e-05
MA1125.1_ZNF3840.0209687
PRDM6_HUMAN.H11MO.0.C0.0209687
FOXL1_HUMAN.H11MO.0.D0.0276781
FOXG1_HUMAN.H11MO.0.D0.0545976
FOXJ3_HUMAN.H11MO.0.A0.0967745Not shown
ANDR_HUMAN.H11MO.0.A0.0967745Not shown
MA0679.2_ONECUT10.0967745Not shown
HXC10_HUMAN.H11MO.0.D0.19033Not shown
FUBP1_HUMAN.H11MO.0.D0.19033Not shown

Motif 12/12

Motif IDq-valPWM
TBX15_HUMAN.H11MO.0.D1.9709e-07
KLF16_HUMAN.H11MO.0.D2.44792e-07
SP1_HUMAN.H11MO.0.A2.44792e-07
SP2_HUMAN.H11MO.0.A2.44792e-07
SP3_HUMAN.H11MO.0.B2.44792e-07
MAZ_HUMAN.H11MO.0.A3.21626e-07Not shown
ZN467_HUMAN.H11MO.0.C4.3582e-07Not shown
PATZ1_HUMAN.H11MO.0.C2.3858700000000003e-06Not shown
WT1_HUMAN.H11MO.0.C2.3858700000000003e-06Not shown
VEZF1_HUMAN.H11MO.0.C2.3858700000000003e-06Not shown